Methods, systems and computer program products for processing images generated using Fourier domain optical coherence tomography (FDOCT)

ABSTRACT

Methods, systems and computer program products for managing frequency domain optical coherence tomography (FDOCT) image resolution. A spectrum used to acquire an image of a subject is calibrated and default dispersion correction parameters are set. Default dispersion management parameters associated with a region of the image of the subject are also set. The image of the subject is acquires after setting the default dispersion correction parameters and the default dispersion management parameters. A quality of the acquired image is compared to a quality metric for the acquired image. The dispersion correction parameters are adjusted if the quality of the acquired image does not meet or exceed the quality metric for the acquired image. The acquired image is reprocesses based on the adjusted dispersion correction parameters. The steps of comparing, adjusting and reprocessing are repeated until the acquired image meets or exceeds the quality metric for the acquired image.

CLAIM OF PRIORITY

The present application claims priority from U.S. ProvisionalApplication No. 60/881,201; filed Jan. 19, 2007, the disclosure of whichis hereby incorporated herein by reference as if set forth in itsentirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant number2R44EY015585 awarded by National Institutes of Health, National EyeInstitute. The United States Government has certain rights in thisinvention.

FIELD OF THE INVENTION

The present invention relates to imaging systems and, more particularly,to optical coherence imaging systems.

BACKGROUND OF THE INVENTION

Optical coherence tomography (OCT) is a rapidly advancing imagingmodality with broad applications in biomedical and industrial imaging.Originally reduced to practice at MIT in the early 1990's, the firstgeneration of OCT achieved clinical success as a diagnostic tool forretinal pathologies. This first generation technology relied on a movingreference mirror in a fiber-interferometric geometry. The interferometeroutput a signal whose strength squared was in direct proportion tobackscattered power of light from within the subject under test,specifically at the depth where the path length into the sample matchedthe path length to the movable reference mirror. This technique is knownas a time-domain technique, and advantages in simplicity of architectureand signal interpretation are overcome by a low signal-to-noise ratioand slow imaging speed.

More recently, a new class of OCT imaging technology has emerged thatmay address some of the disadvantages of the time-domain approach.Fourier domain OCT relies on acquiring a frequency-domain signature andpolling the depth of the sample in one pass. This parallel acquisitionof the depth-dependent backscattered light leads to from about 15 toabout 20 dB increase in the signal to noise ratio (SNR), and thisadvantage in SNR may readily be turned to faster imaging, higher qualityimages, or both. Commercial systems that deploy Fourier domaintechnology have recently reached the market and are aiming at replacingfirst generation systems with systems that acquire images up to 50 timesfaster.

While this speed increase on its own is valuable to the user, theFourier domain approach offers both additional advantages and additionalcomplexity that offer associated benefits and risks. Generating an imagein the Fourier domain approach requires Fourier-transformation offrequency domain (wavelength or wavenumber) data. Opportunities foroptimizing Fourrier domain OCT imaging systems lie in real-time signalprocessing, image optimization, and management and utilization of thehigh data-content images.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide methods, systems andcomputer program products for managing frequency domain opticalcoherence tomography (FDOCT) image resolution. A spectrum used toacquire an image of a subject is calibrated and default dispersioncorrection parameters are set. Default dispersion management parametersassociated with a region of the image of the subject are also set. Theimage of the subject is acquires after setting the default dispersioncorrection parameters and the default dispersion management parameters.A quality of the acquired image is compared to a quality metric for theacquired image. The dispersion correction parameters are adjusted if thequality of the acquired image does not meet or exceed the quality metricfor the acquired image. The acquired image is reprocesses based on theadjusted dispersion correction parameters. The steps of comparing,adjusting and reprocessing are repeated until the acquired image meetsor exceeds the quality metric for the acquired image.

In further embodiments of the present invention, the default dispersioncorrection parameters may be set zero.

In still further embodiments of the present invention, the step ofacquiring the image is performed before calibrating the spectrum.

In some embodiments of the present invention, a spectrometer used toacquire an image of a subject may be calibrated.

In further embodiments of the present invention, the adjusted dispersioncorrection parameters may be stored as the default dispersion correctionparameters for a class of subjects. In certain embodiments, a secondimage of a same subject or another subject in the class may be processedusing the stored adjusted dispersion correction parameters.

In still further embodiments of the present invention, the acquiredimage may be automatically optimized without any user intervention. Thecomparing, adjusting, reprocessing and repeating may be performedautomatically to optimize the acquired image without any userintervention for a class of subjects.

In some embodiments of the present invention, the processing may bedistributed among two or more central processing units (CPUs) to reduceprocessing time.

In further embodiments of the present invention, the acquired image maybe displayed; a line or frame for optimization may be chosen; a regionon the chosen line or frame may be selected to optimize; and theacquired image may be reprocessed based on the selected region on thechosen line or frame.

In still further embodiments of the present invention, the region of theimage may include a three dimensional volume, a two dimensional frame orsubset of a frame, a line or a subset of a line.

In some embodiments of the present invention, a portion of a targetsubject to be imaged may be selected in a control window using anoverlay tool. The image may be acquired and reprocessed based on theselected portion of the acquired image. The selected portion of theacquired image may have a shape that is quadrilateral, annular, circularor linear. The image may be acquired and reprocessed in real time as theportion of the acquired image is selected in the control window.

Further embodiments of the present invention provide methods, systemsand computer program products of processing data. An image is acquiredand raw frequency domain data associated with the acquired image isstored in a temporary or permanent data archive. The stored frequencydomain data is processed to provide improved images.

In further embodiments of the present invention, the image is obtainedusing a frequency domain optical coherence tomography (FDOCT) system andalgorithms may be applied to the stored raw frequency domain data duringat least one step of a processing pipeline to provide improved imagesacquired using FDOCT. Processing may further include applying fastfourier transform (FFT) algorithms to the stored raw frequency domaindata; applying windowing functions to the FFT algorithms; applyingfiltering functions to the FFT algorithms; applying phase-dependentoperations between time- or space-separated data elements; applyingdispersion compensation algorithms to the stored raw frequency domaindata; applying spectral calibration algorithms to the stored rawfrequency domain data; and/or applying averaging algorithms to thestored raw frequency domain data. The averaging algorithms may bedirectly accessible by a user.

In still further embodiments of the present invention, the averagingalgorithms may include line averaging including acquiring a plurality ofdepth lines in one lateral location for averaging, moving to a nextlateral location, and averaging the plurality of depth lines for eachlocation; moving window averaging including acquiring a lateral scanincluding a plurality of depth lines and averaging a user-specifiedcontiguous subset of adjacent depth lines; and frame registrationaveraging including registration of m lateral scans of n depth lines toeach other and averaging the m complete lateral scans of the n depthlines.

In some embodiments of the present invention, space domain data may bestored along with the raw frequency domain data.

In further embodiments of the present invention, a metadata file may beassociated with the raw frequency domain data. The data may be processedon demand processing into space domain data.

In still further embodiments of the present invention, the image may beobtained using a frequency domain optical coherence tomography (FDOCT)system and the raw frequency domain data and metadata may be storedremote from imaging hardware of the FDOCT and/or the point of analysis.The raw frequency domain data may be processed into space domain dataremotely.

In some embodiments of the present invention, the raw frequency domaindata may include subject specific information and/or hardware specificinformation used to associate the raw frequency domain data to a subjectand/or to a specific piece of imaging hardware.

In further embodiments of the present invention, user controlledprocessing of the raw frequency domain data may be used such thatuser-desired information is extracted or user-desired images aregenerated.

In still further embodiments of the present invention, steps of theprocess may be recorded as metadata such that generated data can berecreated from original raw frequency domain data, original metadata,and/or operationally defined metadata.

Some embodiments of the present invention provide methods, systems andcomputer program products for displaying images acquired using frequencydomain optical coherence tomography (FDOCT) systems. A volume intensityprojection (VIP) image is displayed by displaying a weighted sum ofdepth-dependent data over at least a subset of a lateral scan range. Theweighting may be uniform over an entire depth, uniform over a selecteddepth, or non-uniform over the entire depth or a selected depth. Incertain embodiments, the weighting function is controlled by the userthrough access to two or more control items that indicate a centerposition of summed data and the range of the summed data. The weightingfunction may be controlled by the user through access to two or morecontrol items that indicate on a cross-sectional subset of the data thecenter position of the summed data and the range of the summed data. Therange of the summed data may include boundaries of a uniform sum or aparametric representation of a non-uniform sum.

Further embodiments of the present invention provide methods, systemsand computer program products for obtaining values of optimizeddispersion parameters for a subject using a frequency domain opticalcoherence tomography (FDOCT) system. The optimized subject dispersionparameters may be obtained based on the following equations:D1=dD+Ds;D2=dD+Dr;Ds=(D1−D2)+Dr,where D1 equals the series dispersive terms obtained through imageoptimization of a subject, D2 equals the series dispersive termsobtained through image optimization of a reference sample, dD equals thealgebraic difference between the dispersive terms obtained for thesubject and the reference sample, Ds equals the ordered seriesdispersion terms of the subject and Dr equals the ordered seriesdispersion terms for the reference sample.

In still further embodiments of the present invention, the dispersiveterms Dx may be an ordered set of dispersion terms representative ofmathematical Taylor series expansion of phase terms. The optimizeddispersion parameters of a reference sample may be obtained on a systemused for obtaining values for the subject. The reference sample may havea known set of physical dispersion parameters. The reference sample mayhave a known dispersion, including higher order dispersion including oneor more of group velocity (beta_(—)1), group velocity dispersion(beta_(—)2), and slope of group velocity dispersion (beta_(—)3).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a data processing system according to someembodiments of the present invention.

FIG. 2 is a more detailed block diagram of data processing systemsaccording to some embodiments of the present invention.

FIG. 3 is a control window and an overlay tool according to someembodiments of the present invention.

FIG. 4 is a block diagram illustrating multithreading according to someembodiments of the present invention.

FIGS. 5 through 8 are flowcharts illustrating operations according tovarious embodiments of the present invention.

FIG. 9 is a screen shot illustrating the main window, scan tab accordingto some embodiments of the present invention.

FIG. 10 is a screen shot illustrating the main window, processing tabaccording to some embodiments of the present invention.

FIG. 11 is a screen shot illustrating the main window, display adjusttab according to some embodiments of the present invention.

FIG. 12 is a screen shot illustrating the main window, display optionstab according to some embodiments of the present invention.

FIG. 13 is a screen shot illustrating the main window, resampling tabaccording to some embodiments of the present invention.

FIG. 14 is a screen shot illustrating a Volume Intensity ProjectionWindow to some embodiments of the present invention.

FIG. 15 is a screen shot illustrating a Fixation Control Windowaccording to some embodiments of the present invention.

FIG. 16 is a screen shot illustrating a Control Window according to someembodiments of the present invention.

FIG. 17 is a screen shot illustrating a 3D volume display windowaccording to some embodiments of the present invention.

FIG. 18 is a screen shot illustrating Hardware Dialog, Scan tabaccording to some embodiments of the present invention.

FIG. 19 is a screen shot illustrating Hardware Window, capture tabaccording to some embodiments of the present invention.

FIG. 20 is a screen shot illustrating Hardware window, probe tabaccording to some embodiments of the present invention.

FIG. 21 is a block diagram illustrating the organization of processingsteps as a multi-threaded pipeline and divided among the availableprocessors according to some embodiments of the present invention.

FIG. 22 is a flow diagram illustrating division of the processing stepsamong the pipeline stages according to some embodiments of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention now will be described more fully hereinafter withreference to the accompanying figures, in which embodiments of theinvention are shown. This invention may, however, be embodied in manyalternate forms and should not be construed as limited to theembodiments set forth herein. Like numbers refer to like elementsthroughout the description of the figures.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andthis specification and will not be interpreted in an idealized or overlyformal sense unless expressly so defined herein.

It will be understood that, although the terms first, second, etc may beused herein to describe various elements, these elements should not belimited by these terms. These terms are only used to distinguish oneelement, from another element. Thus, a first element discussed belowcould be termed a second element without departing from the teachings ofthe present invention.

The present invention may be embodied as methods, systems and/orcomputer program products. Accordingly, the present invention may beembodied in hardware and/or in software (including firmware, residentsoftware, micro-code, etc.). Furthermore, the present invention may takethe form of a computer program product on a computer-usable orcomputer-readable storage medium having computer-usable orcomputer-readable program code embodied in the medium for use by or inconnection with an instruction execution system. In the context of thisdocument, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific examples (a nonexhaustive list) of thecomputer-readable medium would include the following: an electricalconnection having one or more wires, a portable computer diskette, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,and a portable compact disc read-only memory (CD-ROM). Note that thecomputer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory.

The present invention is described below with reference to blockdiagrams and/or flowchart illustrations of methods, systems and computerprogram products according to some embodiments of the invention. It isto be understood that the functions/acts noted in the blocks may occurout of the order noted in the operational illustrations. For example,two blocks shown in succession may in fact be executed substantiallyconcurrently or the blocks may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Methods, systems and computer program products for processing andreprocessing images generated using frequency domain optical coherencetomography (FDOCT) will now be discussed with respect to FIGS. 1 through22. Referring first to FIG. 1, an exemplary embodiment of a dataprocessing system 100 suitable for use in an FCODT system in accordancewith some embodiments of the present invention will be discussed. Thedata processing system 100 typically includes a user interface 144, suchas a keyboard, keypad, touchpad or the like, I/O data ports 146 and amemory 136 that communicate with a processor 138. The I/O data ports 146can be used to transfer information between the data processing system100 and another computer system or a network. These components may beconventional components, such as those used in many conventional dataprocessing systems, which may be configured to operate as describedherein.

Referring now to FIG. 2, a more detailed block diagram of the dataprocessing system 100 in accordance with some embodiments of the presentinvention will be discussed. The processor 138 communicates with adisplay 245 via and address/data bus 247, the memory 136 via anaddress/data bus 248 and the I/O data ports 146 via an address/date bus249. The processor 138 can be any commercially available or custommicroprocessor. The memory 136 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 100. Thememory 136 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 2, the memory 136 may include several categories ofsoftware and data used in the data processing system 100: an operatingsystem 252; application programs 254; input/output (I/O) device drivers258; and data 256. As will be appreciated by those of skill in the art,the operating system 252 may be any operating system suitable for usewith a data processing system, such as OS/2, AIX or zOS fromInternational Business Machines Corporation, Armonk, N.Y., Windows95,Windows98, Windows2000 or WindowsXP from Microsoft Corporation, Redmond,Wash., Unix or Linux. The I/O device drivers 258 typically includesoftware routines accessed through the operating system 252 by theapplication programs 254 to communicate with devices such as the I/Odata port(s) 146 and certain memory 136 components. The applicationprograms 254 are illustrative of the programs that implement the variousfeatures of the data processing system 100 included in an FDOCT systemand preferably include at least one application that supports operationsaccording to some embodiments of the present invention. Finally, thedata 256 represents the static and dynamic data used by the applicationprograms 254, the operating system 252, the I/O device drivers 258, andother software programs that may reside in the memory 136.

As illustrated in FIG. 2, the data 256 according to some embodiments ofthe present invention may include images acquired from a subject 250,raw frequency domain data 251, volume intensity projection (VIP) images252, dispersion correction parameters 253 and dispersion managementparameters 255. Although the data 256 only includes five different typesof 250, 251, 252, 253 and 255, embodiments of the present invention arenot limited to this configuration. There may be two or more of any ofthe types of data included in the data 256 or different types of datawithout departing from the scope of the present invention.

As further illustrated in FIG. 2, the application programs 254 mayinclude an image resolution management module 221, a frequency domainmodule 223, a display module 224 and a parameter optimization module 225according to some embodiments of the present invention. While thepresent invention is illustrated, for example, with reference to theimage resolution management module 221, the frequency domain module 223,the display module 224 and the parameter optimization module 225 beingapplication programs in FIG. 2, as will be appreciated by those of skillin the art, other configurations may also be utilized while stillbenefiting from the teachings of the present invention. For example, theimage resolution management module 221, the frequency domain module 223,the display module 224 and the parameter optimization module 225 mayalso be incorporated into the operating system 252 or other such logicaldivision of the data processing system 100. Thus, the present inventionshould not be construed as limited to the configuration of FIG. 2, butis intended to encompass any configuration capable of carrying out theoperations described herein.

Furthermore, while the image resolution management module 221, thefrequency domain module 223, the display module 224 and the parameteroptimization module 225 are illustrated in a single data processingsystem, as will be appreciated by those of skill in the art, suchfunctionality may be distributed across one or more data processingsystems. Thus, the present invention should not be construed as limitedto the configuration illustrated in FIGS. 1 through 2, but may beprovided by other arrangements and/or divisions of function between dataprocessing systems.

In particular, the image resolution module 221 is configured to optimizeFDOCT image resolution. It will be understood that methods, systems andcomputer program products according to some embodiments of the presentinvention, may optimize memory usage for reprocessing or for display onthe display 245. Optimizing for reprocessing may consume a greateramount of memory. Optimizing for display may limit or even preventreprocessing, but also may allow the capture and display of many morelines of data. The specific functionalities of the image resolutionmodule 221 will be discussed further below with respect to the blockdiagram of FIG. 3 and the flowcharts of FIGS. 5 through 7.

The frequency domain module 223 is configured to store raw frequencydomain data and process/reprocess the raw frequency domain data atvarious stages of image processing. Details of the frequency domainmodule 223 will be discussed further below. The display module 224 isconfigured to display images acquired using frequency domain opticalcoherence tomography (FDOCT) systems. In particular, the display modulemay be configured to display a VIP image by displaying a weighted sum ofdepth-dependent data over at least a subset of a lateral scan range aswill be discussed further below. Finally, the parameter optimizationmodule 225 is configured to obtain values of optimized parameters for anFDOCT system as will be discussed further below.

Although embodiments of the present invention are discussed herein withrespect to many processing and display embodiments, embodiments of thepresent invention are not limited to these specific embodiments. Forexample, in some embodiments of the present invention certain advancedmeasurements may be enabled by the features of the present invention. Inparticular, in some embodiments of the present invention, the groupvelocity dispersion (beta_(—)2) and slope of the group velocitydispersion (beta_(—)3) may be measured for a sample under test.

Operations of the image resolution management module 221 will now bediscussed with respect to the flowcharts of FIGS. 5 through 7. Referringfirst to the flowchart of FIG. 5, operations for optimizing FDOCT imageresolution in a spectral domain implementation begin at block 500 bycalibrating a spectrum used to acquire an image of the subject. In someembodiment of the present invention, for example, spectral domain or“spectral radar” implementations, a spectrometer may be used to acquirean image of a subject and may be calibrated. In further embodiments ofthe present invention, for example, swept source implementations, thefrequency-dependent spectrum as acquired in a temporally serial fashionis used to acquire an image of a subject and this may be calibrated.Accordingly, calibrating the spectrum according to some embodiments ofthe present invention encompasses any type of spectral calibration knownto those having skill in the art. As used to acquire an image of asubject. As used herein, “subject” refers to any subject that can beeffectively imaged using and FDOCT system in accordance with someembodiments of the present invention. In some embodiments, the subjectmay be a human eye.

Operations continue at blocks 510 and 520 by setting default dispersioncorrection parameters and setting default dispersion managementparameters associated with a region of the image of the subject,respectively. It will be understood that in some embodiments of thepresent invention, the dispersion correction default may be zero withoutdeparting from the scope of the present invention. Furthermore, “settingdefault management parameters” refers to setting a region to beoptimized and the region may be any subset of pixels. The region of theimage may be, for example, a three dimensional volume, a two-dimensionalframe or subset of a frame, a line or a subset of a line withoutdeparting from the scope of the present invention. Dispersionoptimization and parameters associated therewith are discussed in detailin commonly assigned United States Patent Publication No.US-2007-0258094, filed Apr. 24, 2007 to Brown et al., entitled Methods,Systems and Computer Program Products for Optical Coherence Tomography(OCT) Using Automatic Dispersion Compensation, the disclosure of whichis hereby incorporated herein by reference as if set forth in itsentirety.

In some embodiments of the present invention, the image resolutionmanagement module 221 is configured to provide multipleresolution-optimizing algorithms to provide a user of the FDOCT systemflexibility to choose to optimize on the basis of, for example,processing speed, resolution uniformity versus depth and/or bestresolution at a depth.

Image sharpness is a function of accurate dispersion compensationbetween reference and sample paths. Dispersion compensation to firstorder is accomplished by hardware path matching. This is generallysuitable only to a certain extent, as precise component matching isdifficult in practice, and as each sample to be imaged will, in general,have different dispersive characteristics, i.e., each subject is unique.

Methods, systems and computer program products according to someembodiments of the present invention may provide algorithms that can beapplied to optimize dispersion though an expansion of phase terms in thetransform equations that define the relationship between the Fourier andspatial domains. Two methodologies for applying such phase terms havebeen defined, and are termed the “spectral scaling” and “Hilberttransform” approaches. Other approaches are also possible, for example,as discussed in Unites States Patent Publication No. 2007-0258094,incorporated herein by reference above.

These phase parameters are directly related to higher-order dispersion.As will be discussed further below, in some embodiments of the presentinvention, the software is configured to search for the parameters thatoptimize a metric of image quality. The applied metric may be used toincrease or possibly maximize pixel brightness across the image. Othermetrics may also possible without departing from the scope of thepresent invention. In some embodiments, the exact value of thedispersion parameters is not the important result; rather it is thequality of the obtained image.

In further embodiments of the present invention, the value of theoptimized parameters may be of primary interest. Specifically, becausedispersive properties of materials are, in general, linear opticalproperties, the dispersion of one sample may be determined throughreference to a second subject of known dispersion.

For example, if we let D1 equal the dispersive terms obtained throughimage optimization of a sample and D2 equal the dispersive termsobtained through image optimization of a reference, then if we furtherallow that dD equals the differential dispersion between subject-freereference and sample paths and Ds equals the ordered dispersion of thesample and Dr equals the ordered dispersion of the reference, then wenote that:D1=dD+DsD2=dD+DrDs=(D1−D2)+Dr.These operations may be performed by the parameter optimization module225 of FIG. 2.

In some embodiments of the present invention, the dispersive terms Dxmay be an ordered set of dispersion terms representative of mathematicalTaylor series expansion of phase terms. In some embodiments of thepresent invention, the optimized dispersion parameters of a referencesample may be obtained on a system used for obtaining values for thesubject. In some embodiments of the present invention, the referencesample has a known set of physical dispersion parameters, includinghigher order dispersion including one or more of group velocity(beta_(—)1), group velocity dispersion (beta_(—)2), and slope of groupvelocity dispersion (beta_(—)3).

Referring again to FIG. 5, operations continue at block 530 by acquiringthe image of the subject. The image is acquired sometime after thedefault dispersion correction parameters have been set. The image may beacquired before or after the spectrum is calibrated without departingfrom the scope of the present invention. A quality of the acquired imagemay be compared to a quality metric for the acquired image (block 550).It is determined if the quality metric for the acquired image is met bythe acquired image (block 560). If it is determined that the qualitymetric has not been met (block 560), the dispersion correctionparameters may be adjusted (block 570) and the acquired image may bereprocessed based on the adjusted dispersion correction parameters(block 580). In some embodiments of the present invention, the acquiredimage may be reprocessed in the space domain (equivalently, timedomain). Operations of blocks 550 through 580 may be repeated until theacquired image meets or exceeds the quality metric for the acquiredimage.

If, on the other hand, it is determined that the acquired image meetsthe quality metric for the acquired image (block 560), operations foroptimizing FDOCT image resolution may cease. As will be discussedfurther below with respect to block 690 of FIG. 690. The dispersionparameters used to obtain the image that meets the quality metric may bestored and used for further image of this or another subject. Thesubject may be a member of a class of subjects, and the dispersionparameters of the subject may be used for further imaging of othersubjects within the class. It will be understood that optimal dispersionparameters obtained for one subject or class may not be a betterstarting point for a second subject or class than the default parametersof the FDOCT system.

In embodiments of the present invention that use auto-optimizationfeatures according to some embodiments of the present invention,operations of blocks 550 through 580 of FIG. 5 may be performed withoutany intervention from the user of the FDOCT system.

Referring now to the flowchart of FIG. 6, operations for optimizingFDOCT image resolution begin as discussed above with respect to blocks500 and 510 and 520 of FIG. 5. Operations continue at block 630 byacquiring an image of the subject. In embodiments of the presentinvention discussed with respect to FIG. 6, the acquired image may bedisplayed, for example, on display 245 of FIG. 2. A line or frame orother region for optimization may be chosen (block 637). A region on thechosen line or frame to optimize may be selected (block 643).

In some embodiments of the present invention, the selection associatedwith block 643 may be made by selecting a portion of the image to beacquired 340 in a control window 310 using an overlay tool 330 asillustrated in FIG. 3. In particular, referring to the block diagram ofFIG. 3, the control window 300 provides a picture of the subject 320.The overlay tool 330 can be used to highlight the portion 340 of thesubject 320 that is of interest and that will be scanned. Although theoverlay tool 330 is illustrated as rectangular in FIG. 3, embodiments ofthe present invention are not limited to this configuration. Forexample, the overlay tool 330 may be, for example, square, circular,linear or the like without departing from the scope of the presentinvention. The overlay tool 330 may be used to extend, rotate, move,change or the like where on the subject 320 the scan will occur. Theimage may be reprocessed based on the portion 340 of the acquired imageselected using the overlay tool 330. The overlay tool 330 may beregistered with the sample. Thus, according to some embodiments of thepresent invention the image may be reprocessed in real time (Live) asthe portion of the acquired image 340 is selected in the control window310 using the overlay tool 330.

In some embodiments of the present invention, simple user accessibletools, such as the overlay tool 330, are provided to set up and controlthe multi-dimensional imaging functionality. As discussed above, awindow may be provided in which the central position, lengths in up totwo lateral dimensions, and angle of the scan may be set within the timeframe of one B-scan using a simple graphical tool that may be mouse orjoystick controlled.

In some embodiments of the present invention, within this dynamiccontrol window (scan window 310) is a 2-dimensional image 320representing the object to be imaged. This image may have originateddirectly from a recently acquired OCT scan or any other method toacquire a representation of the object to be scanned, including video orCCD photography.

In some embodiments of the present invention, this reference image inthe dynamic control window is calibrated and scaled to the OCT imagingcontrol in order to effect precise control of the scanning beam.Graphical elements may be used to manipulate the scan control, thesegraphical elements may be overlaid on the representative image, and thegraphical elements may be selected from a selection of graphicalelements, such as lines, squares, circles, rectangles and ellipses orother such graphical elements as may represent the structure of adesired scan pattern.

In some embodiments of the present invention, a series of selectablescan patterns may be provided that are at the immediate user control.Scan patterns may be drawn from the family of M-mode scans, LinearB-scans, rectangular volume scans, radial scans, and annular scans.

In some embodiments of the present invention, scan patterns areestablished by setting a defined set of parameters appropriate to eachscan type, including scan length, scan width, scan angle, scan offset inzero, one, or two directions, the number of lines per scan frame, thenumber scan frames per volume, and the number of volumes per imageacquisition cycle.

In some embodiments of the present invention, a multiplicity of scanpatterns, and specifically one scan pattern per scan type can be set bythe user and persist until actively changed by the user to facilitateobtaining a series of scan types and repeating the scanning withoutresetting the scan patterns.

In some embodiments of the present invention, the parameters from onescan type that may be logically applied to another scan type may be, atthe users discretion, transferred through “one click” to the second scantype.

Thus, some embodiments of the present invention incorporate a dynamicscan feature wherein the scan pattern may be altered on the fly. Undernormal steady state scanning conditions, some embodiments of the presentinvention direct the scanning hardware to continuously output a scanpattern synchronized with data capture.

When a dynamic change is desired during scanning, some embodiments ofthe present invention capture the change, calculate the new scanpattern, and update the hardware. This update may be done during theinactive, retrace period of the scan.

In some embodiments of the present invention, the update may done bycalculating the number of lines required for the update at the presentscan line frequency, waiting until the proper location of the scan isreached, and updating the scan prior to the start of a new scan line.

Referring again the flowchart of FIG. 6, the acquired image may bereprocessed based on the selected region on the chosen line or frame(block 647). A quality of the reprocessed acquired image may be comparedto a quality metric for the acquired image (block 650). It is determinedif the quality metric for the acquired image is met by the acquiredimage (block 660). If it is determined that the quality metric has notbeen met (block 660), the dispersion correction parameters may beadjusted (block 670) and the acquired image may be reprocessed based onthe adjusted dispersion correction parameters (block 680). Operations ofblocks 650 through 680 may be repeated until the acquired image meets orexceeds the quality metric for the acquired image.

If, on the other hand, it is determined that the acquired image meetsthe quality metric for the acquired image (block 660), the acquiredimage meeting the quality metric may be displayed (block 687) on adisplay, for example, display 245 of FIG. 2. The adjusted dispersioncorrection parameters used to acquire the image that met the qualitymetric may be stored as the default dispersion correction parameters(block 690).

Referring now to FIG. 7, operations of processing pipelines according tosome embodiments of the present invention will be discussed. Asillustrated in FIG. 7, operations begin at block 705 by pre-processing.Preprocessing includes capturing spectrometer data and processing thecaptures data into frames. In particular, the spectrometer data iscaptured using a cameralink interface. For example, 12 bit data can becaptured in 16 bit words in 2048 pixel lines at 17 khz, or about a 35Mhz sample rate. The captured data may be processed into image frames.For example, 1024 of the 2048 input samples may be extracted andcomposed into images of 500 lines—10,000 lines and 20-200 inactive linescaptured during galvanometer retrace may be stripped out. In someembodiments, the images may be 1000 lines.

Operations continue at block 715 by converting incoming lines andperforming per-line dc subtraction. In particular, incoming lines may beconverted from 16 bit integers into 32 bit floats. Per-line dcsubtraction may be performed. Each sample location may be averaged overan entire frame and this value may be subtracted from the next frame.

The sample may be resampled and the dispersion parameters may beoptimized (block 725). In particular, the 1024 sample input lines may beresampled into 2048 sample output lines. In pseudocode: for eachi,j=lookup[i]; Out[i]=in[j]*weightA[i]+in[j+1]*weightB[i]. Dispersionparameter optimization as discussed above may be performed.

Dispersion compensation may be performed (block 735). In particular,dispersion compensation may be performed on each of the 2048 samplelines, producing a 2048 sample complex result. This may involveconverting each line to complex floating point and setting the phaseangle for each sample to a fixed value. The phase value is different foreach sample and is computed on-demand above by the optimization routine.

A fast Fourier transform (FFT) may be performed on each of the 2048sample complex float input lines, producing a 1024 sample complexfloating point output line (block 745). Operations continue at block755, by converting the 1024 sample complex floating point input linesinto a 1024 sample real float by computing the magnitude of each sample;taking the log of each sample, multiplying the input line with a gainvector; multiplying the input by a scalar; resampling the input lineinto one of equal or shorter length, for example, resampling each 1024sample input line to a 788 sample output line; taking the phase of theoriginal input to the stage and accumulate it over the number of Dopplerlines if Doppler is being calculated; and averaging the b-scan data fromall previous steps of block 755 if Doppler is being calculated.

The image and Doppler float inputs may be converted to 16 bit integerdata and the output lines may be assembled into image frames (block765). The result may be displayed, for example, the B-Scan or VIP (block775). Specific embodiments discussed above with respect to FIG. 7 areprovided for exemplary purposes only and, therefore, embodiments of thepresent invention are not limited by these examples.

Operations of the frequency domain module 223 will now be discussed.Fourier Domain Optical Coherence Tomography (FDOCT) imaging systemsprovide a unique challenge in data management relative to previousgeneration time-domain optical coherence tomography systems (TDOCT).There are two significant differences in the data pipeline between FDOCTand TDOCT systems. First, as the terminology suggests, there is amathematical transform relationship between the detected signals ofTDOCT and FDOCT. In TDOCT, the detected optical signal is a time varyingsignal directly proportional to backscattering as a function of depth ina target sample. In FDOCT, the detected signal is acquired as a functionof optical frequency, and the depth dependent scattering signatureequivalent to the TDOCT signal is derived only after a Fouriertransformation of the information from frequency to time (where time isrelated to depth through knowledge of the velocity of light). Second,because FDOCT enables data acquisition up to two orders of magnitudefaster than TDOCT, the sheer volume of data that may be acquired scalescommensurately.

Typically, the frequency-domain information itself is not of diagnosticinterest, and the transformed time-domain data is the information to bepresented and stored. As discussed further herein, this data managementmethodology may be wasteful of information uniquely available in thefrequency-domain. Thus, according to some embodiments of the presentinvention, the raw frequency domain data may be stored in a data archivein addition to or instead of storing the space domain data.

Storing the data as acquired in the frequency domain may provide datathat is truly archival. For example, storing the data as acquired in thefrequency domain may be nominally equivalent to a trend in digitalphotography of storing data as acquired off of the digital sensor plane.In digital photography, this data is referred to as RAW data, or as aDigital Negative (.dng, Adobe Systems). Storing data as a DigitalNegative may allow for customized software “development,” whereessential corrections to exposure, contrast, and color, for example, canbe made outside of the camera. This may provide a degree of flexibilityand subsequent image quality that can not be replicated when data isstored in a “developed” form, such as JPEG. Furthermore, the developmentis represented by an algorithm stored as metadata with the DigitalNegative. Multiple developments may be efficiently stored, not byduplicating the image, but by storing multiple versions of thedevelopment metadata (cf Adobe Lightroom). This provides a very spaceefficient process for maintaining an archival data set with user-definedimages.

This concept is extensible to transform data sets, and specifically tothe transform data sets that define FDOCT images as discussed accordingto some embodiments of the present invention. Furthermore, there may beadvantages to preserving the ability to develop images off the originalraw Fourier data (Fourier Digital Negative). For example, theseadvantages may arise from specific information embedded in thefrequency-domain data that typically cannot be readily recovered oncethe data is transformed. For example, spectral intensity and phaseinformation present in the raw Fourier data can be utilized to modifyimages, extract additional information, or increase the effectiveimaging depth.

OCT generates high-resolution depth resolved images using the coherencegating behavior of broadband light interferometry. There is a directinverse relationship between bandwidth and resolution. In this sense,OCT is a colorless technique. Chromatic effects in backscattering can bederived at the expense of resolution by filtering the spectrum beforeFourier transformation. This may be particularly useful in the analysisof specific chromophores in the sample with signatures within the sourcebandwidth. These chromophores may be intrinsic to the sample, or may bespecific contrast agents. Filtering to analyze specific spectral regionsmay reduce depth resolution, but when combined with the high resolutionnon-filtered spectrum, may provide a unique intersection betweenresolution and color. Conversely, this spectral filtering can be used toimprove the effective resolution, at some cost to signal-to-noise ratio,by transforming a non-Gaussian spectral profile to a Gaussian profile,reducing the effect of ringing in the Fourier transform of the spectralshape. This filtering operation is done on the frequency domain data,but can be done post-acquisition, and does not typically require aphysical optical filter in the system.

In general, a single spectral interferogram includes only intensityinformation. Motion within the sample, or generated within theinstrument can be used to generate phase information. FDOCT with phaseinformation may provide a broad toolset for further analysis. Motion,for example, flow, within the sample generates phase information inbackscattering that can be applied to the Doppler measurements of samplevelocity. Doppler measurements are generally derived from sequentiallines scans acquired at one (x,y) position within the sample. Each linehas a phase shift relative to the next owing to flow in the sample.

By way of further example, two additional uses for the phase variationassociated with sample motion are in reduction of phase noise and inmeasurements of bulk sample motion. OCT is an interferometric technique,and interferometric speckle is the major cause of noise that cannot beresolved through increased detector integration. However, speckleoriginates in phase variations smaller than the wavelength of light;motion on this order can be used to average out speckle. Such finemotion can be less than one order of magnitude relative to the targetresolution, and therefore speckle can be reduced or eliminated byinducing motion of this magnitude, or by taking advantage ofmicro-motion that always exists within living biological samples. Thisaveraging is best performed in the frequency domain between data withphase variations on the order of speckle. Multiple modes of frequencydomain averaging are possible, constrained only by the acquisitionprotocol used, and relative phase variation between data to be averaged.Examples of averaging modes include A-scan averaging and B-scanaveraging. A-scan averaging can be accomplished on a series of staticacquisitions, for example, (x₁,y₁)=(x₂,y₂), in which case the phasevariation originates in sample motion between integration periods. Thismethod is equivalent to averaging the signals acquired for Dopplerprocessing, which can be referred to as “Doppler averaging.” Thisaveraging mode may have limited effect in speckle reduction on a stablesample, but may also have a low risk of introducing undesirableartifacts. Alternatively, a moving window average can be implemented,where (x₂,y₂)=(x₁+e,y₂+d), in which the phase variation includes acomponent originating in differences between different sample positions.This averaging mode can be quite effective in reducing speckle, but atthe expense of introducing blurring artifacts. A third approach istime-separated averaging. In this case, a series of A-scans that form a2-dimensional image, or B-scan, are acquired and averaged with the nextsequential B-scan. For example, averaged line pairs (x1,y1), (x2,y2) arerelated by (x2,y2)=(x1+e+t,y1+d+t), where t is long enough such thatfluctuations in system phase are sufficient to reduce speckle onaveraging, and (e, d) are intentionally or unintentionally imposed, butsmall enough to minimize undesirable blurring.

In some embodiments of the present invention, alternatively to averagingout this phase noise, measurements of the phase changes have utilitybeyond Doppler flow measurements. In many cases, the subject under testhas intrinsic bulk motion that is desirable to measure. One example isin regard to pulsation of the eyeball with the circulatory cycle. In 3Dimaging the anterior of the eye, it is necessary to correct for thepulsatory motion. A direct method may take advantage of the cyclicalphase variations to provide a correction formula for this motion.

It is well known that the addition of quadrature phase information toFDOCT signals enables the elimination of complex-conjugate artifacts andmirror images endemic to Fourier transformation of real (phase-free)signals. This complex-conjugate artifact reduction process typicallyrequires specific phase information, and the operation occurs in thefrequency domain.

One critical operation in the frequency domain is linearizing theacquired signal to wavenumber, or frequency, as the Fouriertransformation process relies on a sequence of data evenly-spaced infrequency. In general, detection is approximately linear in wavelength,and a resampling into frequency is required. A higher order issue is thenonlinearity in wavelength of the spectral acquisition, due to thegrating equation and any inaccuracies in the pixel placement of thedetecting camera. Accurate calibration of the spectrometer for theseeffects is required for subsequent operations, including importantlysoftware dispersion compensation as discussed in—9526-11. Similarcalibrations are required in swept source implementations. Otheroperations in the frequency domain may include operations relative topolarization effects, and still other operations may be envisioned. Itis nonetheless clear that significant interaction with the frequencydomain data is desirable to increase the information extraction, andthat archiving of the frequency domain data can preserve the datasetfundamental to frequency domain imaging and FDOCT.

In view of the above, some embodiments of the present invention storeraw Fourier-domain data associated with an image acquired using FDOCT ina data archive. In other words, the raw Fourier-domain data is storedinstead of or along with the space domain data. Thus, according to someembodiments, this raw Fourier-domain data may be processed to provideimproved images acquired using FDOCT systems. Processing of the rawFourier-domain data may include applying alternative or new algorithmsrelevant to any portion of the Fourier processing pipeline, i.e., theraw Fourier data can be processed at any point in the processingpipeline.

For example, new fast Fourier transforms (FFTs) algorithms, for example,FFT algorithms optimized for speed or to reduce aliasing, may be appliedto the raw Fourier-domain data. In some embodiments of the presentinvention, windowing and filtering functions may be applied to FFTalgorithms. The windowing and filtering functions may reduce oreliminate artifacts in the time domain data, improve computing speed andmay allow selection of a subset of data that includes/excludes spectralfeatures of interest. Thus, windowing may provide the ability to focuson particular features in an area of interest. In certain embodiments,windowing functions may be applied to two or more (multiple) spectralfeatures and then, the results may be compared or a ration of the twomay be determined. In some embodiments of the present invention,dispersion compensation algorithms may be applied to the rawFourier-domain data. Dispersion compensation algorithms are discussed indetail in commonly assigned United States Patent Publication No.US-2007-0258094, filed Apr. 24, 2007 to Brown et al., entitled Methods,Systems and Computer Program Products for Optical Coherence Tomography(OCT) Using Automatic Dispersion Compensation, which has beenincorporated by reference herein above. In some embodiments of thepresent invention, spectral calibration algorithms and/or averagingalgorithms may also be applied to the raw Fourier-domain data.

In some embodiments of the present invention, frequency or space domainaveraging functions may be embedded in the software application anddirectly accessible by the user.

In some embodiments of the present invention, three averaging modes maybe addressable by the user: Line averaging, whereby the scanning systemacquires a multiplicity of depth lines in one lateral location foraveraging and then moves to the next lateral location, and themultiplicity of depth lines per location are averaged; Moving windowaveraging, whereby the scanning system acquires a typical lateral scanincluding a multiplicity of depth lines and the averaging entailsaveraging a user-specified contiguous subset of neighboring depth lines;and Frame registration averaging, whereby in complete lateral scans of ndepth lines are registered to each other and averaged.

In some embodiments of the present invention, the user can turn theaveraging feature on or off before or after a scan is acquired, subjectto the appropriate lines and frames having been acquired.

In some embodiments of the present invention, the spectrum may bepiecewise analyzed beginning with the stored raw frequency domain datafor determining wavelength-dependent effects and the depth resolution istraded-off for spectral resolution.

In some embodiments of the present invention, the archived raw frequencydomain data may be reprocessed using “one-click” functionality toevaluate the impact of any of the aforementioned features andmeasurements, or the impact of any new or alternative algorithms, or tobe analyzed by as yet unconsidered techniques at any time following theinitial measurement with no loss of information from the as-acquiredstate.

It will be understood that the examples of algorithms and functionsdiscussed above are provided for exemplary purposes only and, therefore,embodiments of the present invention are not limited by these examples.Any functions or algorithms known to those of skill in the art may beapplied to the stored raw Fourier-domain data without departing from thescope of the present invention.

It will be further understood that storing the raw Fourier-domain datamay allow users to optimize the acquired image for their own FDOCTsystems. For example, many optimization algorithms and functions exist.Some embodiments of the present invention allow for software plug-insincluding new algorithms to be inserted in the processing pipeline.

Referring now to the flowchart of FIG. 8, operations for processing dataobtained using an FDOCT system begin at block 803 by acquiring an imageusing FDOCT. The raw frequency domain data associated with the acquiredimage in a data archive is stored (813). The stored frequency domaindata is processed to provide improved images acquired using FDOCT (block823).

In some embodiments of the present invention, processing may includeapplying algorithms to the stored raw frequency domain data during atleast one step of a processing pipeline to provide improved imagesacquired using FDOCT. Processing may further include applying FFTalgorithms to the stored raw frequency domain data; applying windowingfunctions to the FFT algorithms; applying filtering functions to the FFTalgorithms; applying dispersion compensation algorithms to the storedraw frequency domain data; applying spectral calibration algorithms tothe stored raw frequency domain data; and/or applying averagingalgorithms to the stored raw frequency domain data.

In some embodiments of the present invention, applying averagingalgorithms may include applying averaging algorithms that are directlyaccessible by a user. The averaging algorithms may include lineaveraging including acquiring a plurality of depth lines in one laterallocation for averaging, moving to a next lateral location, and averagingthe plurality of depth lines for each location; moving window averagingincluding acquiring a lateral scan including a plurality of depth linesand averaging a user-specified contiguous subset of adjacent depthlines; and frame registration averaging including registration of mcomplete lateral scans of n depth lines to each other and averaging them complete lateral scans of the n depth lines.

In some embodiments of the present invention, the space domain data maybe stored along with the raw frequency domain data. As discussed above,some embodiments of the present invention provide for storage andarchiving of the frequency domain data. A metadata file may beassociated with the frequency domain data to allow on-demand processinginto space domain data. Some embodiments of the present invention maycreate a client-server architecture whereby the frequency-domain dataand metadata may be stored away from the imaging hardware and/or thepoint of analysis, and may be processed into space domain data remotely.

Frequency domain metadata may include scan operation, spectralcalibrations, and phase rules that allow for on-demand spectralfiltering and phase operations. The frequency domain metadata mayfurther include subject specific information as necessary tounambiguously tie the data to the subject. Furthermore, the frequencydomain metadata may include subject specific information as necessary tounambiguously tie the data to the subject while retaining privacy asrequired by law.

Some embodiments of the present invention may provide very fast orvirtually immediate processing from the frequency domain to the spacedomain. Relevant information for this class of processing may beincluded in the metadata. A sequence of one or more frequency domainprocessing steps may be user-defined to perform specific transformoperations to extract user-desired information or generate user-desiredimages.

The frequency domain processing steps may be recorded as metadata, suchthat the so generated data can be recreated from the original frequencydomain data, the original metadata, and the operationally definedmetadata.

In some embodiments of the present invention, the storage of image datamay include the archival frequency domain data, the original archivalmetadata sets, operational metadata sets that are sufficient to recreateany space domain image so processed, and any images created through theuser-defined operations as specified for storage by the user.

It will be understood that client-side or web-based software may be ableto process any of this available data without reference to or connectionwith the original imaging hardware.

Thus, in some embodiments of the present invention, the rawFourier-domain data and its associated calibration tables may be theprimary archived information set stored in the data archive for FDOCT inorder to provide the most reliable and reusable historical record of theimaged subject.

In some embodiments of the present invention, methods are applied toprovide real-time control and processing of the Fourier-to-spatialdomain data by efficient control of processor functionality. Forexample, some embodiments of the present invention use multithreadingtechniques to decrease processing times. As used herein,“multithreading” refers to efficiently splitting and pipelining theprocessing steps involved in the image processing to increase the imagethroughput and reduce image latency.

For example, as illustrated in FIG. 4, 1000 FFT processes 400 may besplit among multiple CPUs, for example, four CPUs 401-404, so that theprocessing is done in parallel to decrease overall processing time. EachFFT process 400 must be done by a single CPU. However, if a particularimage requires, for example, 1000 FFT processes 400, 250 processes maybe allocated to each CPU 401, 402, 403 and 404, which may cut theprocessing time to 25% of processing times provided by a single CPU.

Although embodiments of the present invention illustrates that theprocessing is divided among 4 CPUs, the processing may be divided amongthree or less or more than four CPUs without departing from the scope ofthe present invention.

As discussed above, OCT engines according to some embodiments of thepresent invention generate 20,000 or more lines of spectral data eachcontaining 2048 or more samples per second. This data may go through alarge number of complex processing steps in order to produce spatialdata representing the reflected light in the subject at each point indepth. This processing is typically far beyond the capabilities of thefastest single processor computers available today.

Thus, in order to take advantage of the multiple-processor computersavailable, the processing steps in some embodiments of the presentinvention are organized as a multi-threaded pipeline and divided amongthe available processors as illustrated in FIG. 4.

A block diagram illustrating organization of processing steps as amulti-threaded pipeline and divided among the available processors and aflow diagram illustrating division of the processing steps among thepipeline stages according to some embodiments of the present inventionare illustrated in FIGS. 21 and 22, respectively.

Operations of the display module 224 will now be discussed. In someembodiments of the present invention, certain image processing featuresmay be simplified for the user. For example, in some embodiments of thepresent invention, axial slices (c-scans) of volumetric data may bedisplayed and controlled without reverting to a volumetric renderingengine. A Volume Intensity Projection (VIP) or Summed Voxel Projection,may be displayed by displaying a weighted sum of the depth-dependentdata over all or a subset of the lateral scan range, where the weightingmay be uniform over the entire depth, uniform over a selected depth, ornon-uniform over all or a selected depth.

In some embodiments of the present invention, the weighting function iscontrolled simply by the user through the access of two or more controlitems that indicate, for example, on a cross-sectional subset of thedata, the center position of the summed data and the range of the summeddata, where the range may be the boundaries of a uniform sum, or may bea parametric representation of a non-uniform sum as, for example, thestandard deviation of Gaussian weighting factor applied to thesummation.

As discussed above with respect to FIG. 3, some embodiments of thepresent invention provide tools that may allow modification of the imagein real time. In particular, control items may be applied to modifyingthe center or range in essentially real-time for immediate feedback tothe user. The control items may include a selection tool for setting theweighting function to be applied to the summation.

VIPs are discussed in commonly assigned United States Patent PublicationNo. 2007-0025642, filed Jul. 31, 2006, entitled Methods, Systems AndComputer Program Products For Analyzing Three Dimensional Data SetsObtained From A Sample, the contents of which is hereby incorporatedherein by reference as if set forth in its entirety.

FIGS. 9 through 20 illustrate exemplary screen shots according to someembodiments of the present invention.

In the drawings and specification, there have been disclosed embodimentsof the invention and, although specific terms are employed, they areused in a generic and descriptive sense only and not for purposes oflimitation, the scope of the invention being set forth in the followingclaims.

1. A Fourier Domain Optical Coherence Tomography (FDOCT) imageprocessing system, the system comprising: a display; a processorconfigured to transform a frequency domain spectral interferogram into aspatial domain image using both subject-specific metadata andhardware-specific metadata; and a storage module configured to store thefrequency domain spectral interferogram; wherein the subject-specificmetadata contains information associated with one of a subject and aclass of subjects; wherein the hardware-specific metadata containsinformation associated with one of a hardware configuration or class ofhardware configurations; and wherein the display is configured todisplay spatial domain image data.
 2. The system of claim 1, wherein thesubject-specific metadata includes dispersion correction parameters. 3.The system of claim 2, wherein the processor is further configured toapply the dispersion correction parameters in the transformation of thefrequency domain spectral interferogram to the spatial domain image. 4.The system of claim 3, wherein the processor is further configured to:optimize dispersion correction parameters; and reprocess the frequencydomain spectral interferogram using the improved optimized dispersioncorrection parameters to provide an optimized spatial domain image. 5.The system of claim 1, wherein the hardware-specific metadata includesspectral calibration data for correlating frequency domain data elementsto corresponding optical frequencies.
 6. The system of claim 5, whereinthe processor is further configured to apply the spectral calibrationdata in the transformation of the frequency domain spectralinterferogram to the spatial domain image.
 7. The system of claim 1,wherein the processor is further configured to reduce image speckleusing time-separated averaging of spatial domain data.
 8. The system ofclaim 1, wherein the processor is further configured to apply a spectralfilter to the frequency domain spectral interferogram prior totransforming the frequency domain data to the spatial domain image. 9.The system of claim 8, wherein the processor is further configured tocompare the spatial domain image derived from the application of a firstspectral filter to the spatial domain image derived from the applicationof a second spectral filter.
 10. A method of processing a Fourier DomainOptical Coherence Tomography (FDOCT) image, the method comprising:retrieving a frequency domain spectral interferogram from anon-transitory storage module; retrieving subject-specific metadatacontaining information associated with one of a subject and a class ofsubjects; retrieving hardware-specific metadata containing informationassociated with one of a hardware configuration and a class of hardwareconfigurations; processing the frequency domain spectral interferograminto a spatial domain image using the retrieved subject-specific andhardware-specific metadata, wherein at least one of the retrieving afrequency domain spectral interferogram, retrieving subject-specificmetadata, retrieving hardware-specific metadata and processing isperformed by at least one processor in an Optical Coherence Tomographyimaging processing system.
 11. The method of claim 10, whereinprocessing further comprises applying dispersion correction parametersto the frequency domain spectral interferogram to derive the spatialdomain image.
 12. The method of claim 10, wherein processing furthercomprises: optimizing dispersion correction parameters; and reprocessingthe frequency domain spectral interferogram using the optimizeddispersion correction parameters to provide an optimized spatial domainimage.
 13. The method of claim 10, wherein the processing furthercomprises averaging of spatial domain data elements acquired atapproximately a same location but separated in time to introducefluctuations in phase to reduce speckle on averaging.
 14. The method ofclaim 13, further comprising collecting a sequential series oftwo-dimensional frames of data and averaging frames of data to providethe time separation.
 15. The method of claim 10, wherein processingfurther comprises applying a spectral filter to the frequency domaindata spectral interferogram to transform the frequency domain spectralinterferogram to the spatial domain image.
 16. The method of claim 15,wherein processing further comprises comparing the spatial domain imagederived from the application of a first spectral filter to the spatialdomain image derived from the application of a second spectral filter.